1. Field of the Invention
The present invention pertains in general to modeling and forecasting of data. In particular, the present invention is directed to automatic evaluation of patterns in time-series data.
2. Background of the Invention
A conventional approach to statistical modeling and forecasting is to use pre-existing knowledge of the data's behavior; to determine, using subject-matter experts' knowledge, how trended the data are, what seasonal variations in the data are observable, and what the level shifts and outliers signify. In some cases, mathematical curve-fitting is used to determine the trend; in some cases Fourier analysis is used to determine the frequency of seasonal variation. However, events such as outliers and level-shifts play a very important role in model quality and, if unaccounted for, may offset the uncertainty of the model and forecast, rendering it potentially meaningless. Yet events are typically either not determined or are determined using a subject-matter-expert's subjective opinion.
A traditional approach used in data analysis and forecasting has been trial and error, i.e., by running a data set through a series of models and determining which model fits best; an iterative approach to model tuning is sometimes also used. That includes, as an example, the so-called ARIMA model, wherein seasonality and trend are determined by fitting different trend and seasonal models until a best fit is found.